Startups operating AI services struggle to quantitatively assess copyright litigation risk from their training data. Manually tracking the license status of each dataset source takes hundreds of hours, and there is no standardized report to explain risk to insurers when purchasing coverage, making premium calculations opaque.
When an AI service operator inputs their training dataset list (URL, source, collection date), the system: (1) automatically classifies copyright license status by source (CC, fair use, unauthorized, unknown), (2) calculates litigation risk scores by jurisdiction (Korea/US/EU), (3) matches with domestic insurers' (Samsung Fire, DB Insurance, etc.) tech liability insurance products to auto-generate estimated premium quotes. Provides quarterly risk change tracking reports.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |